LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
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UNVERDICTED 3representative citing papers
APLICUR uses one modest sketch to adaptively update a CUR preconditioner interleaved with LSQR iterations, delivering convergence guarantees independent of sketch size for general large-scale least-squares problems.
PALM-KQR combines inexact ADMM warm-start with semismooth Newton ALM and low-rank preconditioning to solve large-scale kernel quantile regression more efficiently than prior solvers.
citing papers explorer
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Accelerating Regularized Attention Kernel Regression for Spectrum Cartography
LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
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Adaptive LSQR Preconditioning from One Small Sketch
APLICUR uses one modest sketch to adaptively update a CUR preconditioner interleaved with LSQR iterations, delivering convergence guarantees independent of sketch size for general large-scale least-squares problems.
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Scalable Kernel Quantile Regression: A Preconditioned Augmented Lagrangian Method
PALM-KQR combines inexact ADMM warm-start with semismooth Newton ALM and low-rank preconditioning to solve large-scale kernel quantile regression more efficiently than prior solvers.